Lyft (LYFT) Operating Expenses (2018 - 2026)
Lyft (LYFT) posted Operating Expenses of $1.8 billion for Q2 2026, up 13.3% from $1.59 billion a year earlier and up 8.5% from the prior quarter.
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Analysis
Lyft (LYFT) Operating Expenses (2018 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Lyft was $6.89 billion, up 11.8% year-over-year; for FY2025, it came in at $6.5 billion, up 10.2% from FY2024.
- Annual Operating Expenses shows a five-year compound annual growth rate of 9.3% (FY2020 to FY2025).
- In prior years, Lyft's Operating Expenses was $5.9 billion in FY2024 (+21.0%), $4.88 billion in FY2023 (-12.2%), $5.55 billion in FY2022 (+27.9%) and $4.34 billion in FY2021 (+4.1%).
- The Q2 2026 figure stands as the highest quarterly Operating Expenses since Q1 2019.
- On a year-over-year basis, Operating Expenses has increased in each of the last ten quarters, with growth averaging 14.5% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q4 2022, with growth of 42.4%; the weakest was Q4 2023, with a decline of 27.5%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $1.66 billion (Q1 2026), $1.78 billion (Q4 2025) and $1.66 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Alphabet | 4,164.76 Bn | 3,922.28 Bn | 73.85 Bn | 79.03 Bn |
| 2 | Meta Platforms | 1,823.40 Bn | 1,525.92 Bn | 49.47 Bn | 42.03 Bn |
| 3 | Netflix | 288.27 Bn | 248.47 Bn | 6.52 Bn | 1.51 Bn |
| 4 | Alibaba Group Holding | 252.72 Bn | 70.58 Bn | 15.11 Bn | -5.17 Bn |
| 5 | Shopify | 186.52 Bn | 163.71 Bn | 1.71 Bn | 1.22 Bn |
| 6 | Uber Technologies | 139.11 Bn | 111.09 Bn | 6.38 Bn | 12.30 Bn |
| 7 | Booking Holdings | 123.13 Bn | 56.18 Bn | - | 4.85 Bn |
| 8 | PDD Holdings | 111.72 Bn | -140.21 Bn | 9.45 Bn | -5.39 Bn |
| 9 | AppLovin | 103.35 Bn | 93.38 Bn | 1.70 Bn | 429.41 Mn |
| 10 | Lyft | 5.63 Bn | -1.71 Bn | 917.12 Mn | 1.80 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 1.80 Bn |
| Mar 31, 2026 | 1.66 Bn |
| Dec 31, 2025 | 1.78 Bn |
| Sep 30, 2025 | 1.66 Bn |
| Jun 30, 2025 | 1.59 Bn |
| Mar 31, 2025 | 1.48 Bn |
| Dec 31, 2024 | 1.52 Bn |
| Sep 30, 2024 | 1.58 Bn |
| Jun 30, 2024 | 1.46 Bn |
| Mar 31, 2024 | 1.34 Bn |
| Dec 31, 2023 | 1.28 Bn |
| Sep 30, 2023 | 1.20 Bn |
| Jun 30, 2023 | 1.18 Bn |
| Mar 31, 2023 | 1.22 Bn |
| Dec 31, 2022 | 1.77 Bn |
| Sep 30, 2022 | 1.34 Bn |
| Jun 30, 2022 | 1.36 Bn |
| Mar 31, 2022 | 1.07 Bn |
| Dec 31, 2021 | 1.24 Bn |
| Sep 30, 2021 | 1.07 Bn |
API Access
Lyft Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=LYFT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "LYFT", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=LYFT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();